Face Recognition System: Performance Improvement using a Novel Method for Illumination Normalization

As one of the most successful applications of image analysis, face recognition has received significant attention, especially during past few years. Automatic human face recognition has received substantial attention from researchers in biometrics, pattern recognition and computer vision communities. The machine learning and computer graphics communities are also increasingly involved in face recognition. The localization of human faces in digital images is a fundamental step in the process of face recognition. Although the existing automated machine recognition systems have certain level of maturity, but their accomplishments are limited due to real time challenges. For example, face recognition for the images which are acquired in high contrast with different levels of illumination is a critical problem.? It is known that image variation due to lighting changes is larger than that, due to different personal identity, because lighting direction alters the relative gray scale distribution of a face image. In handling these types of practical scenarios, the system must be robust enough to deal with dynamic changes in lighting, hence it is equally important to preprocess the images prior to actual processing and experimentations. This paper proposes a novel method of illumination normalization based on histogram of an image and scaling function. It helps in construction of an optimal global lighting space from these images which improve accuracy of face recognition system. The proposed method helps in recognition of sparsely sampled images with different lighting too. Also, most valuable information of an image, i.e. gray scale value, is not discarded and person’s discriminative information in face image is strengthened. Hence recognition can be carried out using preserved illumination invariant features.